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Net-zero energy transition in ASEAN countries: The evolutionary model brings novel perspectives to the cooperative mechanism of climate governance

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 351卷 119999页

作者： Hu, Yang Weng, Lingfei Chongqing Univ Sch Publ Policy & Adm 174 Shazheng Rd Chongqing 400044 Peoples R China

In an era marked by escalating climate change, the fragile ecological balance faces increasing strain. Whilst significant knowledge exists regarding the accumulation of carbon emission within the Association of Southeast Asian Nations, little is known about when and how countries could reach net-zero emission goal as agreed in Paris Agreement. For this purpose, our study examines the primary driving factors of carbon emission from 1990 to 2020 across the ten countries using the Logarithmic Mean Divisia Index model. We leverage the random forest model to explore the net-zero scenarios and the Autoregressive Integrated Moving Average approach to identify the evolutionary trajectories of carbon emission trends. Our findings underscore the imperative need for expediting decarbonization efforts, emphasizing the urgency for widespread adoption of clean technologies and substantial investment in green initiatives. Countries at similar stages of progress might establish a cooperation mechanism of clean energy base construction, energy storage allocation and policy formulation. These insights can help us better estimate future demand of clean energy, explore strategies for decarbonization, and inform historical commonalities of carbon emission growth.

关键词： Net-zero emissions Energy transition machine learning algorithms Scenario analysis Evolutionary patterns

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Enhancing Software Fault Prediction Through Feature Selection With Spider Wasp Optimization Algorithm

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IEEE ACCESS 2024年 12卷 105309-105325页

作者： Das, Himansu Das, Swarnava Kumar Gourisaria, Mahendra Bhatia Khan, Surbhi Almusharraf, Ahlam Alharbi, Abdullah I. Mahesh, T. R. KIIT Deemed be Univ Sch Comp Engn Bhubaneswar 751024 Odisha India Univ Salford Sch Sci Engn & Environm Dept Data Sci Salford M5 4WT England Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Princess Nourah bint Abdulrahman Univ Coll Business Adm Dept Management POB 84428 Riyadh 11671 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21589 Saudi Arabia JAIN Fac Engn & Technol Sch Comp Sci Bengaluru 562112 India

Software fault prediction (SFP) is a critical focus in software engineering, aiming to enhance productivity and minimize costs by detecting faults early. Feature selection (FS) is pivotal in SFP, enabling the identification of pertinent features for fault prognosis. Existing Feature Selection methods face challenges such as high computational complexity and poor generalization. This paper introduces Feature Selection using Spider Wasp Optimization (FSSWO), a novel FS approach employing the Spider Wasp Optimization (SWO) algorithm, specifically designed for SFP. FSSWO selects optimal feature subsets inspired by spider wasps' behavior. The proposed FSSWO approach is compared with several existing feature selection algorithms, namely FS using Genetic Algorithm (FSGA), FS using Particle Swarm Optimization (FSPSO), FS using Differential Evolution (FSDE), and FS using Ant Colony Optimization (FSACO). Using eleven benchmark datasets, the performance of the proposed FSSWO technique has been assessed and contrasted with its equivalent. The results of the proposed FSSWO approach provide comparable and even superior results to the existing algorithms. The significance of the results has been statistically validated using Friedman and Holm tests. The statistical result of the proposed FSSWO approach reveals that the performance of proposed FSSWO models is improved which leads to better quality software at reduced costs.

关键词： Classification algorithms machine learning algorithms Accuracy Software systems Reviews machine learning Feature extraction Software engineering Spider Wasp optimization algorithm feature selection wrapper method software fault prediction

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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2012年第2期5卷 354-379页

作者： Bioucas-Dias, Jose M. Plaza, Antonio Dobigeon, Nicolas Parente, Mario Du, Qian Gader, Paul Chanussot, Jocelyn Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Grenoble Inst Technol GIPSA Lab Grenoble France Univ Tecn Lisboa Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal Univ Extremadura Dept Technol Comp & Commun Hyperspectral Comp Lab Caceres 10003 Spain Univ Toulouse IRIT INP EN SEEIHT TeSA Inst Natl Polytech Toulouse IRIT LabENSEEIHTSignal & Commun Grp Toulouse France Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

关键词： Hyperspectral imaging hyperspectral remote sensing image analysis image processing imaging spectroscopy inverse problems linear mixture machine learning algorithms nonlinear mixtures pattern recognition remote sensing sparsity spectroscopy unmixing

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Teaching Networks to Solve Optimization Problems

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IEEE ACCESS 2024年 12卷 17102-17113页

作者： Liu, Xinran Lu, Yuzhe Abbasi, Ali Li, Meiyi Mohammadi, Javad Kolouri, Soheil Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA Univ Texas Austin Dept Civil Architectural & Environm Engn Austin TX 78712 USA

Leveraging machine learning to facilitate the optimization process is an emerging field that holds the promise to bypass the fundamental computational bottleneck caused by classic iterative solvers in critical applications requiring near-real-time optimization. The majority of existing approaches focus on learning data-driven optimizers that lead to fewer iterations in solving an optimization. In this paper, we take a different approach and propose to replace the iterative solvers altogether with a trainable parametric set function, that outputs the optimal arguments/parameters of an optimization problem in a single feed forward. We denote our method as learning to Optimize the Optimization Process (LOOP). We show the feasibility of learning such parametric (set) functions to solve various classic optimization problems including linear/nonlinear regression, principal component analysis, transport-based coreset, and quadratic programming in supply management applications. In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the LOOP . Finally, through various numerical experiments, we show that the trained solvers could be orders of magnitude faster than the classic iterative solvers while providing near optimal solutions.

关键词： Optimization Iterative methods Training Neural networks Principal component analysis machine learning algorithms Linear programming machine learning for optimization deep set learning

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Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector machines

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IEEE ACCESS 2020年 8卷 161188-161202页

作者： Shi, Beibei Heidari, Ali Asghar Chen, Cheng Wang, Mingjing Huang, Changcheng Chen, Huiling Zhu, Jiayin Jiangsu Univ Zhenjiang First Peoples Hosp Affliated Peoples Hosp Zhenjiang 212000 Jiangsu Peoples R China Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Nanjing Med Univ Clin Res Ctr Affliated Wuxi Peoples Hosp Wuxi 214023 Jiangsu Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Med Univ Lab Anim Ctr Wenzhou 325035 Peoples R China

Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer

关键词： Support vector machines Optimization Prediction algorithms machine learning algorithms Rats machine learning Benchmark testing Di-2-Ethylhexyl phthalate hepatotoxicity support vector machine Harris hawks optimization salp swarm algorithm grey wolf optimizer

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Histogram Feature-Based Approach for Walking Distance Estimation Using a Waist-Mounted IMU

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IEEE SENSORS JOURNAL 2020年第20期20卷 12354-12363页

作者： Pham, Thanh Tuan Suh, Young Soo Univ Ulsan Dept Elect Engn Ulsan 680749 South Korea

This paper presents a new method of walking distance estimation using an inertial measurement unit (IMU) placed on the user's waist. When the sensor is mounted on the waist, the walking steps can be easily detected. However, step length estimation is a challenging task. In this paper, a walking distance estimation method based on histogram features is proposed. The histogram features, including the uniform and logarithmic quantizations, are derived from each step segment of the acceleration norm data. Then, machine learning algorithms such as support vector regression (SVR), gaussian process regression (GPR), and linear regression (LR) are applied based on the histogram features to estimate the walking distance. Two experiments are conducted to evaluate the walking distance estimation accuracy, where test walking paths consist of a straight line corridor (80 m) and a rectangular path (about 1282 m). Experimental results show that the average absolute errors using the SVR model are 0.76% for straight line corridors and 1.14% for the rectangular paths. The proposed method is shown to outperform existing methods from comparison study.

关键词： Legged locomotion Acceleration Histograms Feature extraction Sensors Estimation machine learning algorithms Histogram feature machine learning regression model inertial sensors walking step detection step length estimation

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Redefining Data Transparency: A Multidimensional Approach

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COMPUTER 2019年第1期52卷 16-26页

作者： Bertino, Elisa Merrill, Shawn Nesen, Alina Utz, Christine Purdue Univ Comp Sci W Lafayette IN 47907 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Ruhr Univ Bochum Dept Elect Engn & Informat Technol Bochum Germany

The use of big data combined with powerful machine-learning algorithms raises major concerns over potential adverse effects. Consequently, data transparency is critical for many data-intensive applications. We provide a comprehensive definition, elaborate on various concerns, and articulate an initial road map for critical research challenges.

关键词： Big Data learning Artificial Intelligence Big Data Potential Adverse Effects Data Transparency Data Intensive Applications Multidimensional Approach machine learning algorithms Law Data Privacy machine learning algorithms Big Data

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Explainable Artificial Intelligence for Smart Grid Intrusion Detection Systems

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IT PROFESSIONAL 2022年第5期24卷 18-24页

作者： Yayla, Alper Haghnegahdar, Lida Dincelli, Ersin Univ Tampa Tampa FL 33606 USA Univ North Texas Denton TX 76203 USA Univ Colorado Denver CO 80202 USA

A popular approach to overcome the complexity of cybersecurity and sophistication of cyber attacks is implementing artificial intelligence (AI)-based security controls that integrate machine learning (ML) algorithms into security controls, such as intrusion and malware detection. These AI-based security controls are considered more effective than traditional signature-based and heuristics-based controls. However, the growing adoption of advanced ML algorithms is turning these AI-based security controls into black-box systems. We postulate that these black-box AI methods would make risk management and informed decision-making challenging. Using smart grid intrusion detection as our context, we illustrate our arguments by outlining a risk assessment plan to discuss the transparency and interpretability of an AI-based security control. We contribute to the literature by changing the focus from performance to explainability of algorithms, highlighting critical steps in explainability for integrating into risk assessment planning, and outlining the implications of explainability in AI-based intrusion detection systems.

关键词： Industries Privacy machine learning algorithms Closed box Intrusion detection Control systems Turning

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Descriptor Generation and Optimization for a Specific Outdoor Environment

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IEEE ACCESS 2020年 8卷 52550-52565页

作者： Takacs, Andras Toledano-Ayala, Manuel Dominguez-Gonzalez, Aurelio Pastrana-Palma, Alberto Talavera Velazquez, Dimas Manuel Ramos, Juan Alejandro Rivas-Araiza, Edgar Univ Autonoma Queretaro Fac Ingn Santiago De Queretaro 76010 Mexico

Modern outdoor self-localizing computer vision applications require descriptors more than repeatability. The descriptors need to be invariant to light conditions and transformation changes to give support for efficient classification. This paper investigates a new framework based on the genetic algorithm to create and optimize extensible modular descriptors for specific outdoor environments. The algorithm returns descriptors with improved efficiency and classification performance. It controls the image processing and machine learning parameters and optimizes the descriptor size by activating the necessary modules. To show the strength of the descriptor, we compared it with the most commonly used standard descriptors on speed, accuracy, and invariance to light conditions, image resolution changes, scale, affine transformation, rotation, and classification. The results show that it has an average result in transformation invariance, and its description ability in sparse areas is significantly better than that of the most-used descriptors. The descriptor was also integrated into an augmented reality algorithm to create a self-regulating segmentation application.

关键词： Genetic algorithms Genomics Bioinformatics machine learning algorithms Optimization Sociology Statistics Image processing descriptor random forest machine learning computer vision genetic algorithms

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Vehicle type detection and passenger satisfaction analysis using smartphone sensors and digital surveys

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第10期13卷 1499-1506页

作者： Perttula, Arto Nhan Nguyen Collin, Jussi Jokinen, Jani-Pekka Tampere Univ Technol Lab Pervas Comp Tampere Finland Aalto Univ Dept Comp Sci Espoo Finland

Detailed knowledge of passenger context is essential for developing intelligent transportation systems. For example, automated ticket sales and personal routing require more information about used means of transportation than traditional time tables can offer. Here, the authors contribute to this topic by using measurements from smartphone sensors to predict (i) whether a person is inside a bus, (ii) if the person is travelling in a diesel or an electric bus, and (iii) how the person is rating the quality of the bus ride. All three tasks are worked out by using a selection of machine learning (ML) algorithms. In tandem with sensor data, collecting a digital passenger survey was conducted to add passengers' own evaluation of the quality of their bus ride. The tests showed that the context of a passenger can be predicted relatively well. However, the prediction of passenger satisfaction is a complex task that requires further research. This research aims to give a good premise for such efforts.

关键词： smart phones road vehicles traffic engineering computing customer satisfaction intelligent transportation systems mobile computing travel industry learning (artificial intelligence) transportation detailed knowledge passenger context intelligent transportation systems automated ticket sales personal routing traditional time tables smartphone sensors electric bus bus ride machine learning algorithms sensor data digital passenger survey passengers passenger satisfaction vehicle type detection digital surveys

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